When Christopher Nolan was promoting his previous film *Interstellar*, he made the casual observation that “Take a field like economics for example. [Unlike physics] you have real material things and it can’t predict anything. It’s always wrong.” There is a lot more truth in that statement than most academic economists would like to admit.

Alan Jay Levinovitz recently put forth the provocative argument that economics is “The New Astrology.” He notes that “surveys indicate that economists see their discipline as ‘the most scientific of the social sciences.’” But unfortunately “real-world history tells a different story, of mathematical models masquerading as science and a public eager to buy them, mistaking elegant equations for empirical accuracy.”

Indeed, Levinovitz goes on to observe that,

The failure of the field to predict the 2008 crisis has also been well-documented. In 2003, for example, only five years before the Great Recession, the Nobel Laureate Robert E Lucas Jr told the American Economic Association that ‘macroeconomics ... has succeeded: its central problem of depression prevention has been solved’. Short-term predictions fair little better — in April 2014, for instance, a survey of 67 economists yielded 100 per cent consensus: interest rates would rise over the next six months. Instead, they fell. A lot.

There are, of course, many other examples of the failure of mathematical models in economics. The model Christina Romer put together during the height of the Great Recession concluded that unemployment could go as high as 8.8 percent *without* the economic stimulus bill. With the stimulus, unemployment went over 10 percent. The spectacular failure of Long Term Capital Management, which was built solely upon investing on mathematical models, is another great example. Indeed, Daniel Kahneman found the “correlations was .01” when asked to evaluate the investment outcomes of 28 different advisors. Warren Buffet is currently crushing the hedge fund Protégé Partners in their ten year, one million dollar bet. (Buffett picked an index fund that invests in the S&P 500.) Finance and economics are linked at the hip in this overconfident, mathematical malaise it would seem.

Returning to Levinovitz, the problem as he sees it is that these highly complicated models built with mystifying and ingenious mathematical equations are completely useless if they are erected upon false assumptions. You may have built the most luxurious mansion imaginable, but if you built it on a hill of sand it might as well be a house of cards.

Think of the Ptolemaic model of the universe that put the Earth at the very center. The Ancient Greeks would notice that the stars would move across the sky, then stop, then go backward, then start moving forward again. To resolve this conundrum, Claudius Ptolemaeus put together an ingenious model of “circles within circles.” Each star not only orbited around the Earth along a given trajectory, but also maintained a secondary orbit around a point moving along the first orbit to make it appear from the Earth that the star would sometimes move backward.

The geocentric model of the universe was a stupendous mathematical achievement, but alas, it was all for naught given the assumptions it was built on were completely false.

Levinovitz uses the example of astrology, noting that,

As an extreme example, take the extraordinary success of Evangeline Adams, a turn-of-the-20th-century astrologer whose clients included the president of Prudential Insurance, two presidents of the New York Stock Exchange, the steel magnate Charles M Schwab, and the banker J P Morgan. To understand why titans of finance would consult Adams about the market, it is essential to recall that astrology used to be a technical discipline, requiring reams of astronomical data and mastery of specialised mathematical formulas. “An astrologer” is, in fact, the Oxford English Dictionary’s second definition of “mathematician.” For centuries, mapping stars was the job of mathematicians, a job motivated and funded by the widespread belief that star-maps were good guides to earthly affairs. The best astrology required the best astronomy, and the best astronomy was done by mathematicians — exactly the kind of person whose authority might appeal to bankers and financiers.

When Adams was eventually arrested in 1914 for laws that forbade astrology, “it was her mathematics that eventually exonerated her.” And this is by no means just a Western phenomenon. Another example the author references is the similarly mathematically impressive work done regarding *Li *in Ancient China*. *Li was also a mathematical model of the stars and for whatever reason, thought to be “essential to good governance.”

Obviously it wasn’t, but the Chinese spent* *“astronomical sums refining mathematical models of the stars.” As we do with much that passes for economics today.

Interestingly enough, Levinovitz quotes several famous Keynesian and neo-classical economists, including Paul Romer, who criticized the “Mathiness in the Theory of Economic Growth” and the man who’s always right (except when he isn’t) Paul Krugman. In this instance, though, Krugman is mostly correct observing that “As I see it, the economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth.”

But this reliance on math to hide the underlying flaws in an economic theory sounds like it falls perfectly in line with “The Pretense of Knowledge” that Friedrich Hayek warned about all those years ago.

Since then, many economists believed they had made economics into a scientific discipline based on modeling and empirical testing. They assured us that by using copious amounts of data and fine-tuned mathematical models they could centrally plan an economy, eliminate the business cycle and increase economic growth and prosperity. And they were wrong.

Surprisingly, Levinovitz does not use the word “econometrics” because that’s the first thing that came to my mind while reading his essay. The econometric approach may be the best example of the mathematical arrogance Levinovitz describes. The flaws in its internal reasoning become obvious, however, as you peel away the math, as Robert Murphy shows,

The econometric approach to stock price movements is analogous to a meteorologist who looks for correlations between various measurements of atmospheric conditions. For example, he might find that the temperature on any given day is a very good predictor of the temperature on the following day. But no meteorologist would believe that the reading on the thermometer one day somehow caused the reading the next day; he knows that the correlation is due to the fact that the true causal factors — such as the angle of the earth relative to its orbital plane around the sun — do not change much from one day to the next.

Unfortunately, this distinction between causation and correlation is not stressed in econometrics. Indeed, for economists truly committed to the positive method, there can be no such distinction. Although the econometric pioneers may understand why certain assumptions are made and can offer a priori justifications such as “rational expectations” for the details of a particular model, the students of such pioneers are often caught up in the mathematical technicalities and lose sight of the true causes of economic phenomena.

But more fundamentally, as Austrian economist Frank Shostak notes, “In the natural sciences, a laboratory experiment can isolate various elements and their movements. There is no equivalent in the discipline of economics. The employment of econometrics and econometric model-building is an attempt to produce a laboratory where controlled experiments can be conducted.”

The result is that economic forecasts are usually just wrong.

Levinovitz believes there is a conflict of interest at the heart of academic economics. He approvingly quotes one economist saying “The interest of the profession is in pursuing its analysis in a language that’s inaccessible to laypeople and even some economists. What we’ve done is monopolise this kind of expertise.” And furthermore, “…that gives us power.”

But it’s more than even just that. It’s not just that economists fails to make accurate predictions or that hedge funds fail to beat the market. If economics is unable to provide bureaucrats with the ability to effectively guide and control an economy, the best alternative would be to turn it back over the market. It’s not just that “mathiness” gives economists “power.” In many ways, it’s the façade that justifies a large number of them having jobs in the first place.

It appears that Levinovitz hasn’t quite grasped the full consequences of the argument he has espoused; namely that because economics models are mostly useless and cannot predict the future with any sort of certainty, then centrally directing an economy would be effectively like flying blind. The failure of economic models to pan out is simply more proof of the pretense of knowledge. And it’s not more knowledge that we need, it’s more humility. The humility to know that “wise” bureaucrats are not the best at directing a market — market participants themselves are.